developing a global road safety model - unece€¦ · developing a global road safety model george...

Developing a Global Road Safety Model

George Yannis1, Eleonora Papadimitriou1, Katerina Folla1

Nenad Nikolic2, Eva Molnar2

1Department of Transportation Planning and Engineering, NTUA, Greece2UN Economic Commission for Europe, Sustainable Transport Division, Switzerland

Washington, D.C., 10 January 2018

NHTSA / ITF-IRTAD – Analysis of International Road Safety data

Background

George Yannis, Professor NTU Athens

• Road accidents constitute a major social

problem in modern societies, with road traffic

injuries being estimated as the eighth leading

cause death globally.

• Particularly in low and middle income countries,

road traffic injuries are twice those in high

income countries and still increasing.

• UN Decade of Action: need to strengthen

global and national efforts for casualty

reduction through evidence-based approaches.

Objective


• To develop a macroscopic road safety decision

making tool that will assist governments and

decision makers, both in developed and

developing countries, to decide on the most

appropriate road safety policies and measures

in order to achieve tangible results.

• Based on work carried out in the framework of

the “Safe Future Inland Transport Systems

(SafeFITS)” project of the United Nations

Economic Commission for Europe (UNECE),

financed by the International Road Union (IRU).

Conceptual Framework


Based on the five pillars of WHO Global Plan of

Action (WHO, 2011) and an improved version of

the SUNflower pyramid (2002):

SafeFITS layers

1. Economy and Management

2. Transport Demand and Exposure

3. Road Safety Measures

4. Road Safety Performance Indicators

5. Fatalities and Injuries

SafeFITS pillars

1. Road Safety Management

2. Road Infrastructure

3. Vehicle

4. User

5. Post-Crash Services

Architecture of the Database


• Data from the five layers and the five pillars

• International databases explored: WHO, UN,

IRF, OECD, etc.

• Data for 130 countries with population

higher than 2,8 million inhabitants

• Data refer to 2013 or latest available year

Economy and Management


Demographic and Economic Characteristics• Population (World Bank Database)

• Area (World Bank Database)

• GNI per capita in US dollars (World Bank Database)

• Projected GDP per capita for 2015-2030 in 2010 US dollars

(ERS International Macroeconomic Data Set)

Road Safety Management Indicators (WHO)• Existence of RS lead agency

• The lead agency is funded

• Existence of national RS strategy

• The RS strategy is funded

• Existence of RS fatality targets

Transport Demand and Exposure


Roads

• Road network density (IRF)

• Percentage of motorways (IRF)

• Percentage of paved roads (IRF, CIA)

Vehicles (IRF)

• Number of vehicles in use in total and by type

of vehicle

Traffic (IRF)

• Traffic Volume

• Inland surface passengers transport

• Inland surface freight transport

Road Safety Measures (1/2)


Roads (WHO)• Road safety audits on new roads

• Existence of speed law

• Max speed limits on urban roads (no speed limits; >50 km/h; ≤50 km/h)

• Max speed limits on rural roads (no speed limits; 100-120 km/h;

70-90 km/h; ≤70 km/h)

• Max speed limits on motorways (no speed limits; ≤100 km/h;

100-120 km/h; ≥120 km/h)

Vehicles• Existence of ADR law (UNECE)

• Vehicle standards include seat-belts, electronic stability control,

pedestrian protection (WHO)

• New cars subjected to NCAP (WHO)

Post-crash care (WHO)• Training in emergency medicine for doctors

• Training in emergency training for nurses

Road Safety Measures (2/2)


Road User (WHO)• Existence of drink-driving law

• Allowed BAC limits (3 separate variables for general

population, young/novice drivers, commercial drivers)

• Existence of national seat-belt law

• The seat-belt law applies to all occupants

• Existence of national child restraint law

• Existence of national helmet law

• The law requires helmet to be fastened

• The helmet law defines specific helmet standards

• Existence of national law on mobile phone use while

driving

• The law applies to hand-held phones

• The law applies to hands-free phones

• Existence of penalty point system

Road Safety Performance Indicators


Traffic law enforcement (WHO)• Assessment of effectiveness of seat-belt law enforcement

• Assessment of effectiveness of drink-driving law enforcement

• Assessment of effectiveness of speed law enforcement

• Assessment of effectiveness of helmet law enforcement

Road User (WHO)• Seat-belt wearing rates in front seats

• Seat-belt wearing rates in rear seats

• Helmet wearing rates – driver

Post-crash care• Estimated percentage of seriously injured patients transported

by ambulance (WHO)

• Number of hospital beds per population (World Bank

Database)

Fatalities and Injuries


• Estimated number of road traffic fatalities (WHO)

• Estimated road traffic fatality rates per 100.000

population (WHO)

• Distribution of road traffic fatalities by road user

type (WHO)

• Distribution of road traffic fatalities by gender

(WHO)

• Percentage of road traffic fatalities attributed to

alcohol (WHO)

Database Overview


• Wherever data for 2013 were not available, the latest data

available were used.

• The missing values of each indicator of the countries were

filled with the mean value of the indicator in their regions.

• The respective information of each variable is properly

represented in the database for the statistical process.

• Data for most variables were available for almost all

countries.

• Low data availability is observed for few variables regarding:

• the restraint use rates

• the percentage of fatalities attributed to alcohol

• the distribution of fatalities by road user type

• transport demand and exposure indicators

Data Analysis Methodology

• Two-step approach of statistical modeling:

• Estimation of composite variables (factor

analysis) in order to take into account as many

indicators as possible of each layer

• Correlating road safety outcomes with

indicators through composite variables by

developing a regression model with explicit

consideration of the time dimension

• Model specificationLog(Fatalities per Population)ti = Ai + Log(Fatalities per

Population)(t-τ)+ Bi * GDPti + Ki * [Economy & Management]ti + Li

* [Transport demand & Exposure]ti + Mi * [Road Safety Measures]ti+ Ni * [RSPI]ti + εi

Where [Composite Variable]


Calculation of composite variables – Economy and Management


[Comp_EM] = -0.250 (EM2_lt15yo) + 0.229

(EM3_gt65yo) + 0.228 (EM4_UrbanPop) + 0.224

(EM7_NationalStrategy) + 0.221

(EM8_NationalStrategyFunded) + 0.222

(EM9_FatalityTargets)

Indicator loadings and coefficients on the estimated

factor (composite variable) on Economy and

ManagementComponent

Loadings Score coefficients

EM1_Popdensity ,091 ,029

EM2_lt15yo -,778 -,250

EM3_gt65yo ,714 ,229

EM4_UrbanPop ,709 ,228

EM5_LeadAgency ,284 ,091

EM6_LeadAgencyFunded ,226 ,073

EM7_NationalStrategy ,697 ,224

EM8_NationalStrategyFunded ,626 ,201

EM9_FatalityTargets ,692 ,222

Calculation of composite variables – Transport Demand and Exposure


[[Comp_TE] = 0.161 (TE1_RoadNetworkDensity) +

0.149 (TE2_Motorways) + 0.238 (TE3_PavedRoads) +

0.272 (TE4_VehiclesPerPop) + 0.267 (TE5_PassCars) -

0.221 (TE7_PTW) - 0.117 (TE10_PassengerFreight)

Indicator loadings and coefficients on the

estimated factor (composite variable) on

Transport Demand and Exposure

Component


TE1_RoadNetworkDensity ,497 ,161

TE2_Motorways ,460 ,149

TE3_PavedRoads ,734 ,238

TE4_VehiclesPerPop ,839 ,272

TE5_PassCars ,825 ,267

TE6_VansLorries -,132 -,043

TE7_PTW -,681 -,221

TE8_Vehkm_Total ,269 ,087

TE9_RailRoad ,136 ,044

TE10_PassengerFreight -,360 -,117

Calculation of composite variables - Measures


[Comp_ME] = 0.069(ME2_ADR) +

0.045(ME4_SpeedLimits_urban) +

0.064(ME6_SpeedLimits_motorways) +

0.088(ME7_VehStand_seatbelts) +

0.091(ME8_VehStand_SeatbeltAnchorages) +

0.092(ME9_VehStand_FrontImpact) +

0.091(ME10_VehStand_SideImpact) +

0.090(ME11_VehStand_ESC) +

0.087(ME12_VehStand_PedProtection) +

0.090(ME13_VehStand_ChildSeats) +

0.068(ME15_BAClimits) + 0.068(ME16_BAClimits_young)

+ 0.065(ME17_BAClimits_commercial) +

0.057(ME19_SeatBeltLaw_all) +

0.063(ME20_ChildRestraintLaw) +

0.034(ME22_HelmetFastened) +

0.038(ME23_HelmetStand) + 0.038(ME24_MobileLaw) +

0.035(ME25_MobileLaw_handheld) +

0.038(ME27_PenaltyPointSyst) +

0.040(ME29_EmergTrain_nurses)

Indicator loadings and coefficients on the estimated factor (composite variable) on MeasuresComponent


ME1_RSA ,245 ,025

ME2_ADR ,681 ,069

ME3_SpeedLaw ,229 ,023

ME4_SpeedLimits_urban ,443 ,045

ME5_SpeedLimits_rural ,200 ,020

ME6_SpeedLimits_motorways ,634 ,064

ME7_VehStand_seatbelts ,877 ,088

ME8_VehStand_SeatbeltAnchorages ,906 ,091

ME9_VehStand_FrontImpact ,908 ,092

ME10_VehStand_SideImpact ,904 ,091

ME11_VehStand_ESC ,891 ,090

ME12_VehStand_PedProtection ,862 ,087

ME13_VehStand_ChildSeats ,896 ,090

ME14_DrinkDrivingLaw ,126 ,013

ME15_BAClimits ,670 ,068

ME16_BAClimits_young ,670 ,068

ME17_BAClimits_commercial ,645 ,065

ME18_SeatBeltLaw ,297 ,030

ME19_SeatBeltLaw_all ,570 ,057

ME20_ChildRestraintLaw ,628 ,063

ME21_HelmetLaw ,236 ,024

ME22_HelmetFastened ,334 ,034

ME23_HelmetStand ,379 ,038

ME24_MobileLaw ,375 ,038

ME25_MobileLaw_handheld ,350 ,035

ME26_MobileLaw_handsfree -,295 -,030

ME27_PenaltyPointSyst ,378 ,038

ME28_EmergTrain_doctors ,178 ,018

ME29_EmergTrain_nurses ,399 ,040

Calculation of composite variables - SPIs


[Comp_PI] = 0.144 (PI1_SeatBeltLaw_enf) + 0.155

(PI2_DrinkDrivingLaw_enf) + 0.152

(PI3_SpeedLaw_enf)+ 0.160 (PI4_HelmetLaw_enf)

+ 0.155 (PI5_SeatBelt_rates_front) + 0.146

(PI6_SeatBelt_rates_rear) + 0.150

(PI7_Helmet_rates_driver)+ 0.127

(PI8_SI_ambulance) + 0.116 (PI9_HospitalBeds)

Indicator loadings and coefficients on the

estimated factor (composite variable) on SPIs

Component


PI1_SeatBeltLaw_enf ,756 ,144

PI2_DrinkDrivingLaw_enf ,812 ,155

PI3_SpeedLaw_enf ,795 ,152

PI4_HelmetLaw_enf ,837 ,160

PI5_SeatBelt_rates_front ,811 ,155

PI6_SeatBelt_rates_rear ,766 ,146

PI7_Helmet_rates_driver ,784 ,150

PI8_SI_ambulance ,667 ,127

PI9_HospitalBeds ,607 ,116

Final Statistical Model

The optimal performing model for the

purposes of SafeFITS

• Dependent variable is the logarithm of the

fatality rate per population for 2013

• The main explanatory variables are the

respective logarithm of fatality rate in 2010

and the respective logarithm of GNI per

capita for 2013

• Four composite variables: the economy &

management, the transport demand and

exposure, the measures, and the SPIs


Parameter BStd.

Error

95% Confidence Interval Hypothesis Test

Lower UpperWald Chi-

Squaredf p-value

(Intercept) 1,694 ,2737 1,157 2,230 38,291 1 <,001

Comp_ME -,135 ,0646 -,261 -,008 4,358 1 ,037

Comp_TE -,007 ,0028 -,013 -,002 7,230 1 ,007

Comp_PI -,007 ,0030 -,013 -,001 5,652 1 ,017

Comp_EM ,007 ,0051 -,003 ,017 2,009 1 ,156

LNFestim_2010 ,769 ,0462 ,678 ,859 276,322 1 <,001

LNGNI_2013 -,091 ,0314 -,153 -,030 8,402 1 ,004

(Scale) ,038

Likelihood Ratio 1379,00

df 6

p-value <,001

Statistical Model AssessmentIn order to assess the model, a comparison of the observed and the predicted values was carried out:

• The mean absolute prediction error is estimated at 2.7 fatalities per population, whereas the mean

percentage prediction error is estimated at 15% of the observed value.

• The model is of very satisfactory performance as regards the good performing countries (low

fatality rate) and of quite satisfactory performance as regards the medium performing countries.


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Statistical Model ValidationIn order to validate the model, a cross-validation was carried out with two subsets:

• 80% of the sample was used to develop (fit) the model, and then the model was implemented

to predict the fatality rate for 2013 of the 20% of the sample not used

• 70% of the sample was used to develop (fit) the model, and then the model was implemented

to predict the fatality rate for 2013 of the 30% of the sample not used


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Validation on 30% of the sample

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SafeFITS Model Demonstration

The overall model implementation

includes 3 distinct steps:

• Step 1 – Countries Benchmark

• Step 2 – Forecast with no new

interventions

• Step 3 – Forecast with

interventions


Model limitations and future improvements• The SafeFITS model was developed on the basis of the most recent and

good quality data available internationally, and by means of rigorous

statistical methods. However, data and analysis methods always have

some limitations.

• Data are primarily directed at vehicle occupants and thus, effects on

road safety outcomes of VRUs may not be captured.

• The effects of interventions may not reflect the unique contribution of

each separate intervention. It is strongly recommended to test

combinations of “similar” interventions (e.g. several vehicle standards,

several types of enforcement or safety equipment use rates etc.)

• The factor analysis procedure does not assume or indicate that a direct

causal relationship exists.

• The calibration with new data will be the ultimate way to fully assess the

performance of the model.


Benefits for the Policy Makers

• The first global road safety model to be used for policy support

• Global assessments (i.e. monitoring the global progress

towards the UN road safety targets)

• Individual country assessments of various policy scenarios

• A framework which enhances the understanding of road safety

causalities, as well as of the related difficulties.

• Full exploitation of the currently available global data, and use

of rigorous analysis techniques, to serve key purposes in road

safety policy analysis: benchmarking, forecasting and

intervention testing.

• An important step for monitoring, evidence-base and systems

approach to be integrated in decision-making.


Developing a Global Road Safety Model

George Yannis1, Eleonora Papadimitriou1, Katerina Folla1

Nenad Nikolic2, Eva Molnar2

1Department of Transportation Planning and Engineering, NTUA, Greece2UN Economic Commission for Europe, Sustainable Transport Division, Switzerland

Washington, D.C., 10 January 2018

NHTSA / ITF-IRTAD – Analysis of International Road Safety data

developing a global road safety model - unece€¦ · developing a global road safety model george...

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